- Thread starter noetsi
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I have a logistic regression model. My DV has two levels, 1 and 0. I don't think that is a linear model.

log(p/(1-p)) = beta_0 + beta_1*x_1 + beta_2*x_2

It is linear in the parameter. i.e. the beta:s.

But it has a non-linear link function: the stuff with "log(p/(1-p))". If you solve that equation, that is maninulate it so that you only get "p" on the left hand side, then you will get:

p = (exp(beta_0 + beta_1*x_1 + beta_2*x_2))/(1 + exp(beta_0 + beta_1*x_1 + beta_2*x_2))

If you plot that curve against x_1 (let x_2 be a constant) you will get an S-shaped curve.

You can check if the factor levels of x_1 are approximately on a line:

log(p/(1-p)) = beta_0 + factor(x_1)_i + beta_2*x_2

Anyway, it will maybe be a good enough approximation.

But my original point remains. SAS's documentation says that its LASSO function was not designed for Logistic regression results. I will try to find some examples of their comments.

says that its LASSO function was not designed for Logistic regression results.

But don't make it complicated. If Lasso is to complicated at the moment so drop it. Do some linear regressions and choose a model.